ISSN: 1439-2305

Number 154 – May 2013

SERVICES SECTORS’ CONCENTRATION:

THE EUROPEAN UNION, GREECE, AND

THE NEW ECONOMIC GEOGRAPHY

Astrid Krenz

Services Sectors’ Concentration: the European Union, Greece, and the New Economic

Geography

Astrid KRENZ, University of Goettingen

Abstract

The aim of this article is to investigate services sectors’ concentration in the European Union based on

employment data and to disentangle the sector-specific developments and influential factors over time.

We find that only the financial intermediation, retail trade and water transport sectors are subject to an

increasing level of concentration over time. Moreover, we can detect a strong specialization tendency

in the sectors of tourism and public administration for the Greek economy. Implementing a two-way

fixed effects model, we find that knowledge spillovers as well as externalities arising from

technological similarities appear to be highly significant in explaining services’ concentration patterns

for the European Union. Technological differences as a reason for services’ concentration only appear

to have been important in the period prior to the Single European Market Enactment. Further evidence

is found for the relevance of factor intensity in explaining concentration of non-market services.

Keywords: Concentration, Services, European Union, Knowledge Spillovers, Technological

Similarities

JEL-Code: F14, L80, R12

Acknowledgements

I would like to thank participants at the annual conference of the Verein für Socialpolitik in Göttingen

and at the Economics Department Seminar at College of William and Mary and Ana Abeliansky for

valuable comments.

1. Introduction

Services have become by far the most important branch for generating GDP in the European

countries. Data from Eurostat shows that services constitute about 70 percent of total gross value

added in the EU-27 for the period from 1995 to 2010 (see figure 1).

Figure 1: Gross value added and share of exports/ imports by sectors

However, services’ trade remains at quite a low level compared to manufacturing goods. Data reveals

that services exports/ imports to extra-EU make up just a small share of about 6-10 percent of GDP

from 1995 to 2010, whereas industrial goods’ exports/ imports range between 22-32 percent.

So far, the analysis of services sectors’ concentration patterns has not been very extensive in the

literature. Given the high relevance of services’ contribution to GDP in various countries over the

world, this is quite surprising and therefore demands for a more thorough analysis. The lack of

investigation might be attributed to the difficulties related to the measurement of services’ values and

volumes which manifests in missing services’ data within many data sources. Services are different

from industrial goods for several reasons: their production and consumption usually occur at the same

time and in the same location, and services cannot be stored. Consequently, they are regarded as non-

tradables. This fact alone suggests that one can expect services to be more dispersed than

manufacturing goods because manufacturing goods could be produced in another place than where

being consumed and they can be stored and transported to a place where they are being demanded for,

thus easing clustering processes of manufacturing firms.

Some definitions are necessary before proceeding: the term concentration refers to the localization

of different industries across different countries/ regions. The term agglomeration means that the bulk

of industrial activity is localized in a country or region. Specialization, on the other hand, looks at the

spatial side first and addresses countries’ or regions’ industrial structures. The task of the present

contribution is to investigate concentration tendencies in the European Union.

The issue of concentration has been given attention to in a great range of studies on industrial

sectors (see for example Krugman 1991 a; Kim 1995; Bruelhart 2001; Bruelhart 1998; Paluzie et al.

2001). The European evidence talks about increasing tendencies of industrial concentration (Bruelhart

1998, 2001; Haaland et al. 1999) .

Given the process of European single market liberalization, services activities have become free to

locate anywhere across the member countries of the European Union. The extent of services’

localization in the European Union is in that respect especially worthy to be investigated. However,

there exist only a few studies in the past that particularly deal with services’ concentration in the

regional context of the European Union. Oros and Turcu (2008) find that for the three broad sectors of

agriculture, industry, and services, inequalities in the European Union from 1995 to 2004 became

more pronounced on a regional level of measurement than on a country level. The services sector

exhibits the lowest degree of concentration, and it is showing diminishing concentration tendencies

over time. However, when decomposing the effects between the country and regional levels, the

authors show that concentration fell only in terms of regional measurement, but concentration slightly

increased on the country level. Jennequin (2008) found that at a more disaggregated level, services

sectors experience only a very slight increase in concentration in the European Union over time. Based

on a rather broad level of sectoral disaggregation, finance, insurance, real estate and business services

are most concentrated and transport, storage and communication are least. On a more disaggregated

level, however, it is remarkable that water transport is most highly concentrated and that concentration

increased most over the time from 1991 to 1999 for 7 European countries (Denmark, Finland, France,

Germany, Hungary, Italy and Norway). Midelfart-Knarvik et al. (2000) investigated services’

concentration in the EU considering only five services sectors. The authors find that financial services,

insurance, business, and real estate activities are the sectors that are the most concentrated over time

and also those that deconcentrated most between 1982 and 1995. Transport services are the most

dispersed services over time; in turn this sector shows the highest increase in concentration over time.

In addition, wholesale trade, and retail trade reveal increasing levels of concentration. Hallet (2000)

found that especially the banking and insurance sector and other market services were highly

concentrated between 1980 and 1995. The development over time appeared to be quite stable.

However, the author runs his investigation only on a very broad sectoral classification which

comprises 5 types of services sectors.

Apart from investigating descriptive tendencies of services’ concentration, the literature has also

sought for explanatory factors of concentration, though mainly on manufacturing sectors. Studies for

the European Union on industrial sectors find evidence for increasing returns to scale (Amiti 1998,

1999), intermediate goods intensity (Amiti 1998, 1999), forward and backward linkages (Midelfart-

Knarvik et al. 2000) and the supply of highly skilled workers and researchers (Midelfart-Knarvik et al.

2000).

Clarifying the causes for agglomeration is still a current concern in research. In a recent rigorous

paper, Ellison et al. (2010) investigated the importance of Marshallian externalities1

for co-

agglomeration of manufacturing industries in the US. Their results reveal that natural resources and all

types of the externalities are important explanatory variables. Input-output linkages between firms

have a slightly higher level of importance, supporting new economic geography factors for the

explanation of agglomeration patterns. Kolko (2010) analyzed the importance of Marshallian

externalities in explaining services’ agglomeration and co-agglomeration in the US. He found that

only knowledge spillovers bear significant explanatory power for services’ agglomeration.

The case of services is special since it is well known from the literature that concentration patterns

for some services are strong at the more regional or metropolitan level (Kolko (2010)). The reason for

that is clear: some services are highly dependent on face-to-face contact with the consumer, so

services are dispersed on a very regional level. On the other hand, several services like transportation,

media production or investment banking were shown to be highly agglomerated in the US (see also

Kolko (2010)). Consequently, investigating services concentration on the national, county, state and

more regional level, all have their justification, and the present paper mainly focuses on the national

level and will address some issues on the regional level in one of the later sections, also.

This paper aims to address explanatory factors for services’ concentration in the European Union

for the first time. It remains to be shown whether the ideas of New Economic Geography can explain

developments in services sectors’ concentration, as well. Therefore, explanatory variables will be

derived from the Traditional Trade Theory, New Trade Theory, and the New Economic Geography.

Further, we will control for the influences of knowledge spillovers.

Contrary to past studies, we face less essential restrictions on reducing the time or country

dimension when considering a more detailed degree of services sectors, which is guaranteed by using

the more comprehensive EU KLEMS database. In that way, we can offer a more detailed analysis of

region- and sector-specific tendencies and explanatory factors of services sectors’ concentration in the

European Union. However, especially longer time series on the measure we generate for capturing

knowledge spillovers are still not available in the common data sets. This is the basic data constraint

we had to deal with in our analysis.

The paper is organized as follows. Part 2 deals with the measurement of services

concentration. Part 3 talks about the data set followed up by descriptive analyses in part 4. Part 5

presents the regression analysis and further robustness checks. Part 6 concludes.

2. Measurement of Services Sectors’ Concentration

For the following analysis Gini coefficients will be used as in Krugman (1991 a) or Amiti (1998,

1999). They are calculated as follows. First the Balassa index will be computed by using the formula:

(1)

where denotes services sector s’ employment in country c, denotes total services sectors’

employment in country c, denotes total services sector s’ employment in the European Union, and denotes total services sectors’ employment in the European Union, all at one point in time t. The

Gini coefficient is derived by ranking the Balassa index in descending order, then constructing a

concentration curve by plotting the cumulative of the numerator on the vertical axis and the

cumulative of the denominator on the horizontal axis (cumulating over countries)2

. The Gini

coefficient is equal to twice the area within a 45 degree line and the concentration-curve. The Gini

coefficient equals zero if a services sector is totally equally distributed across countries, concentration

then will be low. It approaches one, the more the Balassa indices differ from one, concentration then

will be high. For reasons of robustness checks, we will also calculate another measure in one of the

later sections.

3. Data Issues

Data are taken from the EU KLEMS Database (2008) and can be downloaded online. EU KLEMS

is a data collection project funded by the European Commission. The data collection has been done

and supported by the OECD, several statistical offices, national economic policy research institutes

and academic institutions in the EU. For computation of the Gini coefficients national employment

data was used. The variable taken was number of persons engaged (in thousands). The application of

employment data rather than trade or value added data, for example, is justified by the difficulties

arising from deteriorating influences of exchange rate and price dynamics. Data covering 14 European

countries and 22 services sectors

was employed. The countries included in the analysis are: Austria,

Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, Spain,

Sweden, UK.

Services are given by the OECD ISIC Rev. 3 classification. A further disaggregation of

countries or services sectors was prevented by lack of data. Moreover, in some of the following

analyses we had to aggregate services sectors due to missing data, such that in a minimum 13 sectors

were considered. Data was available for the time period from 1970 to 2005. Values given in local

currency for Denmark, Sweden and the UK were converted to euros, using the respective exchange

rates at January 4th 1999.3

Furthermore, all countries’ values for explanatory variables were deflated

using the price index for gross output (1995=100) from the EU KLEMS Database. This has been done

in order to cancel out trends just being caused by inflation. Using several price indices for various

variables was prevented by the lack of data.

In a further step, more detailed regional data on employment given by the NUTS II classification

were extracted from Eurostat. Unfortunately, the more detailed sectoral NUTS II employment data

were only available for the years 2000, 2004 and 2008 (Labor costs Survey, NACE Rev.2

classification). Overall, data availability led us to consider 47 regions and 12 services sectors for the

year 2004 only (the list of regions can be found in the appendix).

4. Descriptive Analysis of Services Sectors’ Concentration

The development of services sectors’ concentration in the European Union is shown in the

following table.

Table 1: Services sectors’ concentration over time

Source: Own calculations based on EU KLEMS data (2008).

Note: This table displays the Gini coefficients for the years 1970, 1980, 1990, 1995, 2000, and 2005, as well as the changes in Gini

coefficients from 1970-2005 or 1995-2005, respectively. In the last column results of a linear trend test over time are shown.

Agglomeration 1970 1980 1990 1995 2000 2005 Per cent

change 1970-

2005 (1995-

2005)

Trend test

All services sectors (18 sectors) 0.186 0.1804 0.1653 0.1575 0.1513 0.1458 -0.2161 -0.0012**

(22 sectors) 0.1702 0.1643 0.159 -0.0658 -0.001**

(13 sectors) 0.1182 0.1159 0.1089 0.1 0.0982 0.0997 -0.1565 -0.001**

Sale, maintenance and repair of

motor vehicles and motorcycles;

retail sale of fuel

0.0723 0.0897 0.095 0.0764 0.078 0.0844 0.1674 0.0001

Wholesale trade and commission

trade, except of motor vehicles

and motorcycles

0.1123 0.0922 0.0832 0.0902 0.0862 0.0845 -0.2476 -0.0006**

Retail trade, except of motor

vehicles and motorcycles; repair

of household goods

0.0445 0.0778 0.0635 0.0783 0.0821 0.0784 0.7618 0.0007**

Hotels and restaurants 0.1347 0.155 0.1441 0.1407 0.1437 0.1495 0.1099 -0.00004

Other Inland transport 0.0838 0.0971 0.0895 0.0749 0.0785 0.0755 -0.099 -0.0003**

Other Water transport 0.3521 0.3351 0.3275 0.3873 0.3817 0.3849 0.0932 0.002**

Other Air transport 0.2042 0.2166 0.1806 0.1984 0.1901 0.1784 -0.1263 -0.0004**

Other Supporting and auxiliary

transport activities; activities of

travel agencies

0.1663 0.1367 0.1368 0.1084 0.0949 0.0813 -0.5111 -0.0022**

Post and telecommunication 0.0857 0.0789 0.0805 0.0768 0.0806 0.0923 0.077 0.00001

Financial intermediation, except

insurance and pension funding

0.0545 0.0635 0.0795 0.4587 0.0022**

Insurance and pension funding,

except compulsory social security

0.1852 0.1813 0.1733 -0.0643 -0.0018**

Activities related to financial

intermediation

0.1686 0.1527 0.1344 -0.2028 -0.0023**

Real estate activities 0.2371 0.2614 0.2257 0.1879 0.1782 0.1831 -0.2278 -0.0027**

Renting of machinery and

equipment

0.4578 0.42 0.333 0.2749 0.2446 0.1891 -0.5869 -0.0082**

Computer and related activities 0.3068 0.2746 0.2709 0.2524 0.2241 0.2008 -0.3455 -0.0025**

Research and development 0.4126 0.4106 0.3746 0.3559 0.3556 0.346 -0.1614 -0.0023**

Other business activities 0.1898 0.1772 0.1434 0.1285 0.108 0.0925 -0.5126 -0.003**

Public admin and defense;

compulsory social security

0.1176 0.1003 0.1134 0.1101 0.1224 0.1143 -0.0281 0.0002

Education 0.1047 0.0968 0.0961 0.0901 0.0802 0.0822 -0.2149 -0.0006**

Health and social work 0.1508 0.1472 0.142 0.1294 0.1226 0.1287 -0.1466 -0.0009**

Other community, social and

personal services

0.1147 0.0807 0.0761 0.074 0.0719 0.078 -0.32 -0.0007**

Private households with

employed persons

0.5017 0.493 0.4875 -0.0283 -0.0014**

Transport and Storage 0.0566 0.0758 0.0775 0.0614 0.0552 0.0502 -0.1131 -0.0005**

Financial intermediation 0.1135 0.0645 0.08 0.0676 0.076 0.0869 -0.2344 -0.0008**

Renting of machinery and

equipment, research and

development and other business

activities

0.1922 0.1865 0.1385 0.1171 0.0995 0.0835 -0.5656 -0.0036**

Only data points for 1970, 1980, 1990, 1995, 2000 and 2005, respectively, are displayed for reasons of

lucidity. Further, changes in concentration over time were calculated and a linear trend test was

applied in order to check for the significance of changes.

As can be seen from table 1, total services sectors’ concentration in the European Union decreased

by about 22 percent from 1970 to 2005 (resulting for 18 services sectors, since data are available for

some sectors only from 1995 to 2005). Most of the services sectors display a significant decrease in

concentration (as is the case for wholesale trade, other inland transport, other air transport, other

supporting and auxiliary transport activities, insurance and pension funding, activities related to

financial intermediation, real estate activities, renting of machinery and equipment, computer and

related activities, research and development, other business activities, education, health and social

work, other community and social services and private households with employed persons).

This is in

line with Midelfart-Knarvik et al. (2000) who find financial services, insurance, business and real

estate activities to deconcentrate between 1982 and 1995 — although only slightly — as well.

As it can be further seen, only retail trade, other water transport and financial intermediation except

of insurance and pension funding showed a significant increase in concentration. Concentration of

financial intermediation except insurance and pension funding and retail trade, however, still remains

at a low level, only its change over time is huge compared to all other sectors. This contrasts

Jennequin (2008), as he finds financial services to be highly concentrated.

In the present study, concentration in financial intermediation except insurance and pension funding

records a 46 percent change, concentration in retail trade a 76 percent change, respectively. So, in

contrast to Midelfart-Knarvik et al. (2000) and in accordance with Jennequin (2008) this study can

reveal an increasing level of concentration in financial services.4

Financial services’ and retail trade

concentration points to a change of economic structures in the national landscape of the European

Union. Although financial services and retail trade employees would need to be close in proximity to

customers, apparently over time due to the improved information and communication technologies

(internet, online banking, online sale etc.), clustering of these activities occurred. Concentration in

water transport was quite high in 1970 exhibiting a Gini coefficient of 0.35 which increased to 0.38 in

2005. A high level of concentration for water transport has also been found by Jennequin (2008). The

concentration of water transport is not surprising due to its dependence on waterways. Dispersal of

services which remain in each nation’s responsibility, like education or other community and social

services, is not surprising, either. The most dispersed sectors like post, land transport or education are

sectors that are most influenced by being close to demand, so these sectors necessarily need to be

dispersed and present wherever people live (Jennequin (2008)). These are also sectoral activities that

cannot easily be replaced by internet technologies, but will further depend on face-to-face contact

between providers and customers.

Overall, the studies of Jennequin (2008) and Midelfart-Knarvik et al. (2000) find that knowledge-

intensive business services (KIBS) or finance, insurance, real estate and business services (FIRE),

respectively, are the most concentrated sectors in the EU. The present study, however, employing a

more detailed analysis on a wider range of services sectors, time period and sample of countries, can

detect that financial services are less concentrated across the member states of the European Union,

only their concentration level increased over time. Moreover, this study finds evidence for research

and development and computer services being highly localized across member states of the EU.

However, the sectors deconcentrate heavily over time. The development of computer services’

concentration is in favor of getting closer to customers again. Similarly, in the context of fewer co-

localization of services that rely heavily on information technology in the US, Kolko (2010) talks

about the internet being a substitute for phone, mail, and travel, however not for in-person interactions.

For the following regression analysis, explanatory variables were not available for all of the 22

services sectors. Therefore, an aggregated Gini index for 13 services was computed and sectors had to

be aggregated for transport and storage5

, financial intermediation6

and renting of machinery and

equipment, research and development and other business activities7

. As can be seen from table 1,

information on a high level of agglomeration for water transport and the increase of concentration in

pure financial intermediation thus gets lost.

Investigating in which country services got actually localized delivers further interesting results.

The three countries a sector got most or least concentrated in for the years 1970 and 2005 — as the

ranking of the Balassa index would indicate — are shown in table 2.

Taking a closer look at Greece for reasons of current interest, one can see that in the year 2005

transport and storage activities, hotels and restaurants, retail trade, and sale of motor vehicles were

highly concentrated. In other words, a large share of Greek employees in services sectors was working

in services related to tourism, for example, compared to the employment share of Greece within the

EU. These tendencies are explained by the history and landscape. Tourism has been important for

Greece since ages, since people want to go and see the Greek cultural heritage from the ancient times.

Furthermore, Greece owns the highest share of the world merchant fleet. It amounts to 15.96 percent

of the world fleet’s tonnage (UNCTAD 2010). In addition, the high Greek Balassa Index for education

has to be interpreted as Greece having a higher services’ employment share in that branch than its

employment share in total EU employment would suggest. Overall, the public sector is a large

employer. Mitsopoulos and Pelagidis (2011) highlight that government spending was related to

changing governments over time. Whereas from 1991 to 2004 primary expenses of the central

government were held around 13.7 - 15.6 percent of GDP, from 2004 until 2009 the new government

increased expenditures to 20.5 percent of GDP. The absolute value thus increased enormously since

GDP grew rapidly over time. The authors explain that the significant increase in government spending

was due to wages that had to be paid given that the staff increased in the public sector over time and

because of the needs of social security funds for the public sector employees’ pensions.

Table 2: Services sectors’ concentration evaluated by the Balassa index

Source: Own calculations based on EU KLEMS data (2008).

Note: The three countries with either highest or lowest Balassa index for a services sector for the years 1970 and 2005 are displayed in this

table.

In the EU, the financial services are mostly concentrated in Ireland, UK and Germany. Before the

beginning of the financial crisis, the financial sector of the UK was functioning very well. In its staff

report, the IMF (2007) talks about a steady rise of net exports of financial services of UK from 1995

to 2005 from 10 billions US Dollars to about 35 billions with an especially sharp increase from 2003

to 2005. At that time, the financial services from the UK flourished, the system was said to be open

and flexible to capital flows and ”in a position of strength” (IMF 2007, p. 26).

Health and social work and other community, social and personal services are mostly localized in

the Northern European countries like Denmark, Sweden, Finland, Ireland and the UK. This

Highest Balassa index Lowest Balassa index

1970 2005 1970 2005

Sale, maintenance and repair of motor vehicles and

motorcycles; retail sale of fuel

Denmark

Portugal

Italy

Portugal

Greece

Italy

Sweden

Spain

France

Belgium

Netherlands

Sweden

Wholesale trade and commission trade, except of motor

vehicles and motorcycles

Portugal

Netherlands

Belgium

Portugal

Denmark

Austria

Greece

Spain

UK

Greece

UK

Ireland

Retail trade, except of motor vehicles and motorcycles;

repair of household goods

Ireland

Spain

UK

Greece

Portugal

Spain

Sweden

Netherlands

France

Sweden

Belgium

Finland

Hotels and restaurants Portugal

Austria

Greece

Greece

Spain

Portugal

Netherlands

Sweden

Denmark

Sweden

Denmark

Belgium

Transport and storage Greece

Finland

Spain

Greece

Finland

Austria

Netherlands

France

Italy

UK

Portugal

Netherlands

Post and telecommunications Ireland

Austria

Belgium

Ireland

Finland

Belgium

Spain

Portugal

Netherlands

Portugal

Italy

Netherlands

Financial intermediation Germany

Belgium

Netherlands

Ireland

UK

Germany

Sweden

Italy

Ireland

Finland

Portugal

Sweden

Real estate activities Finland

Portugal

Austria

Finland

Sweden

Austria

Greece

Italy

Ireland

Greece

Italy

Portugal

Renting of machinery and equipment, research and

development and other business activities

Netherlands

France

UK

Netherlands

Belgium

UK

Portugal

Finland

Spain

Portugal

Greece

Spain

Public admin and defense; compulsory social security Germany

Belgium

France

Belgium

Portugal

France

Denmark

Spain

Finland

UK

Netherlands

Sweden

Education Sweden

Belgium

Ireland

Sweden

Greece

France

Spain

Netherlands

Germany

Netherlands

Spain

Austria

Health and social work Sweden

Finland

Denmark

Denmark

Sweden

Finland

Spain

Belgium

Greece

Greece

Spain

Portugal

Other community, social and personal services Italy

Sweden

Ireland

Ireland

Sweden

UK

Portugal

Belgium

France

Belgium

Portugal

France

demonstrates the fact that the Northern European countries (Denmark, Finland and Sweden) possess

big social welfare systems, employing a huge workforce. Andersen et al. (2007) talk about the ”Nordic

Model” as a special economic and social system being existent for the Northern European countries

Denmark, Finland, Iceland, Norway and Sweden. These countries would be comprehensive welfare

states with transfers to households and publicly provided social services, high public and private

spending for child care, education, and research and development, and good labor market institutions,

which comprise strong labor unions, good wage coordination, generous unemployment benefits and

active labor market policies. As it can be seen, the highest share of services’ employment working in

educational services is in fact given in Sweden.

In table 3, a closer look is taken at the level of specialization of the Greek economy in 2005, again

evaluated by using the Balassa indices. For this part of the analysis, measuring specialization, a

slightly different measurement for the Balassa index is taken which is given by the following formula:

(2)

Here, the employment in a given sector s in Greece (G) is considered as a share of Greece’s total

employment and set in relation to the services sector s’ employment share in the whole EU.

Table 3: Greece — Balassa index in 2005

Total EU

services‘ employment

in

thousands

in percent Greece’s services’ employment

in

thousands

in percent Balassa

index

Sale, maintenance and repair of motor

vehicles and motorcycles; retail sale

of fuel

3898.17 3.1 109 4.17 1.34

Wholesale trade and commission

trade, except of motor vehicles and

motorcycles

7559.61 6.01 108 4.12 0.69

Retail trade, except of motor vehicles

and motorcycles, repair of household

goods

14811.8 11.78 427 16.34 1.39

Hotels and restaurants 8558.84 6.81 276 10.58 1.55

Transport and storage 7418.67 5.9 229 8.76 1.48

Post and telecommunications 2539.28 2.02 51 1.96 0.97

Financial intermediation 5068.3 4.03 99 3.79 0.94

Real estate activities 1800.98 1.43 3 0.11 0.08

Renting of machinery and equipment,

research and development and other

business activities

20799.83 16.55 294 11.26 0.68

Public admin and defense;

compulsory social security

11671.86 9.29 303 11.61 1.25

Education 11690.25 9.3 287 10.99 1.18

Health and social work 17012.05 13.53 206 7.87 0.58

Other community, social and personal

services

8490.68 6.75 151 5.79 0.86

Source: Own calculations based on EU KLEMS data (2008).

Note: In this table the decomposition of Balassa indices for Greece based on different levels of specialization is shown. In columns two and

three total EU services’ employment is shown both in levels and in percent, in columns four and five Greece’s services’ employment is

shown both in levels and in percent.

With a Balassa index value of 1.55, most of the Greeks employed in services — that is 10.58

percent — work in the branch of hotels and restaurants, whereas only 6.81 percent of total EU

services’ employment is working in this branch. The Balassa index is also high for (in descending

order): transport and storage, retail trade, sale of motor vehicles, public administration and education.

These facts correspond to results from table 2. One can learn that Greek employment is also explicitly

high in the branch of public administration, indicating the high number of employees in the public

sector.

What do the results obtained above mean for the Greek Economy? The data so far showed that the

Greek economy mainly specialized in services related to tourism, like hotels and restaurants, transport

and storage, retail trade, or services financed by the public sector, like public administration and

education. In 2010 salaries of Greek public sector’s employees were decided to be reduced, public

administration was more and more consolidated and with 2011 public sector’s jobs were decided to be

axed. These undertakings were done and planned in order to increase public savings. As has been seen

from the data, this means a reduction of employment in branches where Greece got particularly

specialized in. In fact, the austerity programs will cause Greek employment and thus the economic

structure to change immensely, which will cause unemployment to increase, negatively affecting the

citizens’ welfare. Another problem arises from the specialization in tourism: in times of economic and

social distress tourists are less willing to come to the country and so another source of income

diminishes.

5. Econometric Analysis

5.1 Explaining Services Sectors’ Concentration

In order to explain services sectors’ concentration, explanatory variables will be derived from two

different branches of trade theories and from the New Economic Geography. These theories point to

different reasons for countries’ specialization or sectoral concentration. In addition, knowledge

spillovers shall be captured in the regression framework. In extracting the possible influential factors

we will follow the literature of Amiti (1999), Haaland et al. (1999), Torstensson (1996), Rosenthal and

Strange (2001), and Kolko (2010), for example. Our measures for factor intensity and intermediate

products intensity are in accordance with those taken by Amiti (1999), our technology measure

resembles the one taken by Haaland et al. (1999)/ Torstensson (1996).

Heckscher-Ohlin theory tells us that a country specializes in producing and exporting that good that

is produced relative intensively with the factor the country is relatively well endowed with. Factor

intensity can be operationalized as:

. (2) denotes labor compensation in millions of euros in services sector s and denotes gross

value added in millions of euros in services sector s at time point t. The measure consists of the

deviation of the share of labor compensation to value added to the services sectors’ average share of

labor compensation to average value added. Taking the absolute value of this measure captures a basic

element of Heckscher-Ohlin’s theory: services sectors exhibiting either a high labor or a high capital

intensity (represented by either high or low labor compensation compared to the European average)

will show up a high level of services sectors’ concentration. A positive influence of fact on services

sectors’ concentration can be expected.

New Trade Theories focus on scale economies in production. Using scale effects, firms can either

produce more output at a given cost or a given output at lower costs. For countries’ economic

structures both divergence and equalization is possible. In case of a homogeneous good divergence

happens through the process of further integration. Countries will specialize in one good which they

will start trading with. In case of a heterogenous good, consumers could get access to a greater variety

of products via free trade through economic integration. Intra-sectoral trade will seize, leading to

equalized sectoral structures across countries. Scale economies shall be captured by the following

measure: . (3) denotes labor compensation in millions of euros in service s at time t, capital

compensation in millions of euros, intermediate inputs in millions of euros and denotes

gross output as a volume index (1995=100). A negative relationship between concentration and scale

intensity can be expected. This is because the more output can be produced at a per unit cost, the lower

will be the measure scale. Increasing returns to scale positively influence concentration, since firms

will want to locate closer to each other in order to reap off scale economies (Krugman 1979; Krugman

1980).

The basic New Economic Geography model (Krugman 1991 b) deals with forward and backward

linkages occurring among firms and workers. If workers move to a region, the rise in their regional

expenditures increases the incentive for firms to locate there, too (home market or market size effect

which constitutes the so called backward linkage). Consequently, firms locating in one place will lead

to goods’ prices to fall — products get cheaper because of competing firms — which increases the

incentive for workers to move to this place (price index effect which constitutes the so called forward

linkage) . A similar way of reasoning holds in the models of Krugman and Venables (1995) and

Krugman and Venables (1996). There, mutual dependencies exist between upstream and downstream

firms. Industries making use of economies of scale will locate at sites where demand is high, usually

this will be in the larger market (backward linkage). They can minimize transport costs this way.

Demand in turn will be high in places where firms are already located in, because their products will

be less expensive (forward linkage). New Economic Geography’s reasoning is going to be modeled in

the following way:

. (4) denotes gross output in millions of euros and is gross value added in millions of euros.

Services sectors that use a lot of intermediate products are expected to show a higher level of

concentration than other services sectors, based on elements of New Economic Geography models

(Krugman 1991 b; Krugman and Venables 1995; Krugman and Venables 1996). Therefore a positive

relationship between concentration and intermediate products intensity is assumed.

As has been

mentioned by Amiti (1999), the measure of intermediate products intensity, however, might only

consider vertical linkages of downstream firms and does not use inputs from firms within sectors. The

last aspect cannot be taken care of here, either, because data are lacking on detailed input-output

structures for services. However, the measure for intermediate products intensity is said to be fair

enough to capture important features of new economic geography theory.

Ricardian trade theory states that differences in production technologies will lead to countries

getting relative production advantages in certain goods, which will induce specialization. Differences

in production technologies shall be measured via different labor productivities:

. (5)

denotes value added in services sector s in country j and denotes employment in services

sector s in country j, where . This measure describes the deviation between the share of

labor productivity in sector s in country j relative to this sector’s labor productivity in the EU and the

share of a country’s labor productivity in relation to all countries’ and sectors’ labor productivity in

the EU. A higher deviation results in a higher value for techdiff and should thus influence services’

concentration positively.

Knowlegde spillovers are not easy to be operationalized. Kolko (2010) , for example, employs the

share of workers with graduate degrees. The author mentions that through this measure, though, not

only knowledge spillovers but also effects of labor market pooling might be captured. Due to limited

data availability, we also used a proxy which is the share of hours worked by highly skilled workers.

The idea behind is that a larger employment of the highly skilled will invoke positive spillovers among

peers, resulting in mutual benefits. We expect that knowledge spillovers (knowspill) influence

services’ concentration positively.

A regression function has been estimated via a two-way fixed effects estimation procedure:

(6)

13 services sectors and 35 time points are considered for the analysis.8

The Gini coefficient is

regressed on the logarithm of factor intensity, scale economies, intermediate products’ intensity,

technology differences, knowledge spillovers, sectoral dummies and time dummies . Time

dummies are taken relative to 1971, services dummies are taken relative to the sector sale,

maintenance and repair of motor vehicles. By the logs of variables the coefficients can be interpreted

as percentage changes in variables. The results are given in table 4.

9

Table 4: Regression results services’ concentration

Source: Own calculations based on EU KLEMS data (2008). Note: Two-way fixed effects estimation. Services sectors and time dummies included. ** denotes significance at

the 5 percent level, * denotes significance at the 10 percent level. White standard errors are taken.

The results in columns 1-3 show that the coefficient for knowledge spillovers is significant and

positive in all specifications. Knowledge spillovers as measured here by the share of hours worked by

high-skilled workers, play an important role in explaining services’ concentration. The coefficient for

intermediate products intensity is significant and positive only in one specification. The influence of

factor intensity on services’ concentration seems to be negligible due to the low negative value of the

coefficient. The coefficient for technology differences does not show the expected sign. Obviously,

services do not concentrate more in case of big technological differences across regions, but in fact

they do if technologies are more similar. So, it is not the Ricardian argument that is supported by this

variable, but some further influence of knowledge spillovers and positive externalities comes through.

The coefficient for scale economies does not bear the expected sign. The positive sign for scale

economies might indicate a situation that has been explained before for the case of a heterogenous

good. Through increasing liberalization consumers get access to a greater variety of products, intra-

sectoral trade increases, economic structures across countries equalize. Another reason prominent in

the industry concentration literature is that benefits through scale economies were already used in

former periods of time (Haaland et al. 1999). The following section, producing robustness checks, will

shed light on these issues.

5.2 Sensitivity Analysis

Robustness of the results shall be tested in the following by using a different measure for the

dependent variable and by dividing the sample into different time periods and differentiating between

effects for market and non-market services, respectively.

The Krugman index as having been taken in Krugman (1991 a) will be calculated as an alternative

ln(gini)

(1)

ln(gini)

(2)

ln(gini)

(3)

ln(Krugman

index)

(4)

ln(Krugman

index)

(5)

constant -0.8608**

(0.1841)

0.1611

(0.5089)

0.075

(0.5086)

-0.5662**

(0.2062)

-0.1426

(0.7079)

ln(factor intensity) -0.0087

(0.0107)

-0.0216**

(0.0102)

-0.0197**

(0.009)

0.0197*

(0.0118)

-0.027**

(0.0118)

ln(scale intensity) 0.3829**

(0.0608)

0.1478*

(0.0875)

0.1822**

(0.0839)

0.3127**

(0.0658)

-0.07

(0.1188)

ln(intermediate products intensity) 0.6236**

(0.0975)

-0.1047

(0.1134)

-0.1055

(0.0999)

0.4984**

(0.1345)

-0.2148

(0.1339)

ln(technological differences) -1.0573**

(0.1344)

-0.9489**

(0.2436)

-0.9486**

(0.1477)

-0.6141**

(-0.6141)

ln(knowledge spillovers)

(1992-2005)

0.8366**

(0.1415)

0.6567**

(0.1399)

0.6738**

(0.1714)

N observations 455 182 182 455 182

R² 0.919 0.965 0.97 0.891 0.948

F-Stat 137.69 383.87 462.98 105.11 236.84

dependent variable. Midelfart-Knarvik et al. (2000) took a slightly different modeling of this measure

which is:

. (7) measures the deviation of the share of services’ employment for sector s in country c relative to

this sector’s total EU employment from the other (S-1) services sectors’ mean of these sectoral shares.

The index is calculated for the 13 sectors described in the text above.

As can be seen from table 5, the same concentration tendencies for services sectors emerge as in

case of taking the Gini coefficient for the 13 sectors. 10

Table 5: Services sectors’ concentration by Krugman index of concentration

Source: Own calculations based on EU KLEMS data (2008).

Note: The table displays values of the Krugman concentration index. Since for 1970 data are not available for financial intermediation,

insurance and pension funding, activities related to financial intermediation and private households, Krugman indices were calculated for 13

services sectors, only, aggregating the three sectors of financial intermediation, transport/storage and renting of

machinery/equipment/research/development/other business activities. Only the five most and least agglomerated services sectors in 1970 and

2005 are shown in this table.

In the next step, regressions are run with the Krugman index as the dependent variable and the

explanatory variables from the former regression framework. Results given in table 4 columns 4 and 5

show that knowledge spillovers appear to be relevant, both measured as direct influence due to higher

shares of hours worked by high-skilled workers and as indirect influence via technology similarity.

The influence of factor intensity appears to be negligible again and the effect of scale economies and

intermediate products intensity is not that clear since the coefficients are not significant in either

specification.

Services sector Krugman

index 2005

Services sector Krugman

index 1970

Most agglomerated

Real estate activities 0.2622 Real activities 0.3357

Hotels and restaurants 0.2343 Renting of machinery and equipment and other

business activities

0.3052

Health and social work 0.189 Hotels and restaurants 0.2282

Public admin and defense, compulsory

social security

0.1794 Public admin and defense, compulsory social

security

0.2074

Wholesale trade and commission trade,

except of motor vehicles and

motorcycles

0.1727 Health and social work 0.1972

Most dispersed

Transport and storage 0.0928 Retail trade, except of motor vehicles and

motorcycles; repair of household goods

0.096

Other community, social and personal

services

0.1063 Sale, maintenance and repair of motor vehicles

and motorcycles; retail sale of fuel

0.0993

Retail trade, except of motor vehicles

and motorcycles; repair of household

goods

0.11 Transport and storage 0.108

Financial intermediation 0.1151 Post and telecommunications 0.1139

Post and telecommunications 0.132 Education 0.1498

Table 6: Robustness checks services’ concentration

Source: Own calculations based on EU KLEMS data (2008).

Note: Two-way fixed effects estimation. Services sectors and time dummies included. ** denotes significance at the 5 percent level, *

denotes significance at the 10 percent level. White standard errors are taken.

Further robustness checks are given in table 6. Dividing the sample into the time of the pre-Single

European Market Enactment (SEME) era (the years until the end of 1985) and the post-SEME era

yields meaningful results. In 1986 the Single European Act was signed which involved the member

countries’ joint effort to collaborate in terms of economic and monetary policy in order to achieve the

goal of economic convergence. These efforts can be expected to have formed the economic structure

in the European Union accordingly. The coefficients for scale intensity and technological differences

bear positive signs and are significant for the pre-SEME period. For the post-SEME period,

intermediate products intensity bears a positive and significant coefficient, technology differences bear

a negative significant coefficient. These results lend support to the interpretation that services

concentration followed Ricardian arguments of specialization in case of technological differences

across countries in the pre-SEME era, but the primary driver for concentration after the launch of the

Single European Act is knowledge spillovers due to the clustering. The bulk of increasing product

variety’s influence on services concentration seems to have been valid in the pre-SEME period,

whereas intermediate products intensity matters only in the post-SEME period. We therefore observe

that over different time periods different influences are at work.

Another check is done based on the division between market and non-market services (see the

classification in the appendix). While the results in columns 8 and 9 imply the pure effect across the

sectoral division of services sectors, results from columns 10 and 11 mix the sectoral dependencies

with time period differences. 11

As such, results in columns 10 and 11 are only difficult to interpret.

Increasing product variety appears to be important for market services’ concentration only, as well as

intermediate products intensity. Factor intensity, however, is relevant for concentration of non-market

ln(Krugman

index)

1971-1985

(6)

ln(Krugman

index)

1986-2005

(7)

ln(Krugman

index)

market

services

(8)

ln(Krugman

index) non-

market

services

(9)

ln(Krugman

index)

market

services

(10)

ln(Krugman

index) non-

market

services

(11)

constant -1.2419**

(0.3014)

-1.2097**

(0.3633)

0.3113

(0.2261)

-0.0478

(0.3944)

0.7427

(0.7839)

-3.1984**

(0.8242)

ln(factor intensity) 0.0211

(0.0155)

-0.0169

(0.0128)

0.0084

(0.0108)

0.3331**

(0.1176)

-0.0281**

(0.0117)

-0.8524**

(0.4042)

ln(scale intensity) 0.6468**

(0.1064)

0.0389

(0.1095)

0.5411**

(0.0699)

-0.2928

(0.2655)

-0.1051

(0.1589)

-0.9213*

(0.4716)

ln(intermediate goods intensity) -0.1969

(0.2092)

0.3363**

(0.1655)

1.2662**

(0.1494)

-0.1469

(0.0971)

-0.4142*

(0.2307)

0.2348

(0.6395)

ln(technological differences) 0.87**

(0.2941)

-1.0266**

(0.1641)

-0.4921**

(0.1446)

-2.6376**

(0.8465)

-0.4348

(0.2642)

ln(knowledge spillovers)

(1992-2005)

1.077**

(0.2531)

0.4069*

(0.226)

N observations 195 260 350 105 140 42

R² 0.934 0.923 0.913 0.871 0.952 0.899

F-Stat 184.56 171.88 113.98 21.36 190.64 19.94

services. This can be nicely interpreted, since the services of education, health and social service are

especially labor-intensive, so clearly services’ concentration depends on labor intensity. Both market

and non-market services’ concentration are influenced by technology similarity and with some

cautious interpretation also by knowledge spillovers. Note, however, that the sample got severely

reduced for the regression shown in column 11, so meaningful inference is hard to be drawn.

Furthermore, for the last specification an investigation showed that multi-collinearity problems arise

from taking up both technology differences and knowledge spillovers variables in the regression. As

such, only the variable capturing knowledge spillovers was finally included.

Table 7: Correlation matrix for further robustness checks of services’ concentration for the year 2004

Source: Own calculations based on EU KLEMS data (2008) and Eurostat.

Note: This table displays Pearson correlation coefficients. The Krugman index in the first data column has been calculated for NUTS II-

level data stemming from Eurostat. Only 12 services sectors were taken (because public administration data were missing for a lot of

regions) and 47 regions

An investigation for more regionally detailed sectoral data was planned as the next step. However, the

NUTS II data available in Eurostat on a more detailed sectoral level meant a reduction to the time

points of 2000, 2004 and 2008. For the year 2000, observations were only very fragmented.

Furthermore, several lacking data points on services sectors also meant a drop-out of several regions in

the sample. Due to these problems, a regression analysis could not be performed. In table 7, however,

simple correlations between the constructed Krugman index on NUTS II employment data for 2004

and the explanatory country-level variables are displayed. In comparison with the Gini coefficient and

the Krugman index for country-level data, a far higher correlation between knowledge spillovers and

concentration is indicated for the NUTS II-level measure of concentration. A simple correlation,

however, does not tell us much about the factor’s influence in a multi-factorial interdependence

system. Thus, with a hopefully better data coverage at hand in the future, further relationships should

be investigated.

6. Conclusion

Clarifying the development of services’ concentration and disentangling its driving forces in the

specific context of the European Union has been the aim of the present contribution. The past literature

has barely investigated trends and explanatory factors of services’ concentration. In fact, so far, there

was no such study investigating the explanatory factors of services’ concentration on a detailed

sectoral level for the European Union over a longer time period. The analysis of this paper is meant to

ln(NUTS II-sample

Krugmanindex)

ln(gini) ln(Krugman index)

ln(factor intensity) 0.3267 0.6581 0.5496

ln(scale intensity) -0.0023 0.1452 0.1914

ln(intermediate goods intensity) -0.4518 -0.4676 -0.4329

ln(technological differences) -0.6753 -0.6840 -0.6181

ln(knowledge spillovers) 0.4113 0.1819 0.0414

fill this gap.

Several interesting results have been derived from the analysis. On average, services sectors in the

European Union underwent a tendency of steady dispersion over time. Only the sectors of retail trade,

water transport and financial intermediation except of insurance and pension funding recorded a

significant increase in concentration.

Taking a closer look at country-specific concentration patterns of services, it could be shown that

the Greek workforce is especially large compared to the European average in the branches of retail

trade, hotels and restaurants, transport and storage, public administration and education. We explained

that the current austerity programs will alter the economic/ employment structure in Greece, causing

unemployment in the branches that Greece got mostly specialized in.

As concerns the explanatory factors for services’ concentration, the regression analysis reveals that

knowledge spillovers play an important role on the country level and apparently also on a more

regional level. Further, services do concentrate more in case of high technological similarity across

countries, not supporting the Ricardian argument, but instead indicating benefits arising from

spillovers and positive externalities. Technological differences as a reason for services’ concentration

only appear to have been important in the pre-Single European Market Enactment period, whereas

intermediate products intensity — representing New Economic Geography factors — seems to matter

only in the period after the enactment of the European Single Market. Moreover, increasing product

variety and intermediate products intensity appear to be important for market services’ concentration

only. For non-market services’ concentration, factor intensity is important, since these services are

highly labor-intensive.

The results point to the relevance of policies enhancing education and R&D, since a proper

training of the workforce will evoke positive knowledge spillovers, fostering the clustering of

economic activity. Clustering, in turn, might give rise to a process of mutual benefits, as is commonly

known from the New Economic Geography literature (see Marshall 1938, Krugman 1991, etc.). The

results further reveal that intermediate products intensity, and as such the linkages between suppliers,

have become stronger after the Single European Market Enactment. Consequently, politics could take

care of a provision of an adequate infrastructure in order to guarantee the flow of intermediates.

In our analysis we faced data constraints as regards the more detailed sectoral regional level. For

future research, one might set up a study investigating various services sectors’ concentration in the

EU once better data become available. Also, the issue of co-agglomeration of services sectors would

deserve a clarification in the context of the European Union. Further, due to the changing landscape of

Europe through increasing efforts of liberalization and enlargement, it would be interesting to consider

particularly the East-European countries.

Appendix

Included NUTS II regions:

Vlaams Gewest, Baden-Württemberg, Bayern, Berlin, Brandenburg, Hamburg, Hessen, Mecklenburg-

Vorpommern, Niedersachsen, Nordrhein-Westfalen, Rheinland-Pfalz, Sachsen, Sachsen-Anhalt,

Schleswig-Holstein, Thüringen, Voreia Ellada, Attiki, Noroeste, Noreste, Comunidad de Madrid,

Centro (Spain), Este(Spain), Sur (Spain), Canarias, Nord-Ovest (Italy), Nord-Est (Italy), Centro

(Italy), Sud (Italy), Isole (Italy), West-Nederland, Zuid-Nederland, Ostösterreich, Südösterreich,

Westösterreich, Centro (Portugal), North East (UK), North West (UK), Yorkshire and the Humber,

East Midlands, West Midlands, East of England, London, South East (UK), South West (UK), Wales ,

Scotland, Northern Irland.

Division between market and non-market services:

Market services: Sale, maintenance and repair of motor vehicles and motorcycles; retail sale of fuel;

Wholesale trade and commission trade, except of motor vehicles and motorcycles; Retail trade, except

of motor vehicles and motorcycles; repair of household goods; Hotels and restaurants; Transport and

storage; Post and telecommunications; Financial intermediation; Real estate activities; Renting of

machinery and equipment, research and development and other business activities

Non-market services: Education; Health and social work; Other community, social and personal

services

Notes

1

Marshall (1938) differentiates three reasons for firms benefitting from clustering: 1. a local labor

pool, which offers a large enough number of workers with adequate skills available for a firm, 2.

information spillovers, that is information is easily available and eases buying and selling decisions for

example, and 3. input-output linkages, where it is assumed that firms will benefit from cheaper inputs

when they are clustered together. Firms can realize economies of scale when clustering in an area

because of the aforementioned reasons. Economies of scale are external to a single firm and internal

for the cluster.

2

Note that in contrast to a commonly known Lorenz-curve the concentration-curve lies on the upper

side of a 45 degree line.

3

See ECB, exchange rate statistics.

4

Note from table 1 (at the bottom) that at a high level of aggregation of financial intermediation the

increase in concentration vanishes.

5

Comprising the sectors other inland transport, other water transport, other air transport, other

supporting and auxiliary transport activities; activities of travel agencies.

6

Comprising financial intermediation except insurance and pension funding, insurance and pension

funding except compulsory social security and activities related to financial intermediation.

7

Comprising renting of machinery and equipment, computer and related activities, research and

development and other business activities.

8

Remember that in constructing the dependent and independent variables values were aggregated over

14 countries.

9

A White test indicated heteroskedasticity of error terms such that White’s heteroskedasticity-

consistent standard errors were calculated.

10

Note that just 13 sectors are taken for analysis here since data are missing for the years until 1995

for several sectors.

11

Note: due to inclusion of the knowledge spillovers variable the data set gets reduced to 1992-2005.

Literature

Amiti, M (1999), ”Specialization Patterns in Europe”, Weltwirtschaftliches Archiv, Vol. 135, No. 4,

pages 573-593.

Amiti, M (1998), ”New Trade Theories and Industrial Location in the EU: A Survey of Evidence”,

Oxford Review of Economic Policy, Vol. 14, No. 2, pages 45-53.

Bayoumi T; Eichengreen, B (1992), ”Shocking aspects of European Monetary Unification”, NBER

Working Paper Series, Working Paper No. 3949.

Bruelhart, M (1998), ”Economic Geography, Industry Location and Trade: The Evidence”, The World

Economy, Vol. 21, No. 6, pages 775-801.

Bruelhart, M (2001), ”Evolving Geographical Concentration of European Manufacturing Industries”,

Weltwirtschaftliches Archiv, Vol. 137, No. 2, pages 215-243.

ECB, exchange rate statistics, see http : //www.ecb.int/stats/exchange/euro fxref /html/index.en.html.

Ellison, G; Glaeser, E L; Kerr, W R (2010), ”What Causes Industry Agglomeration? Evidence from

Coagglomeration Patterns”, American Economic Review, Vol. 100, pages 1195-1213.

EU KLEMS Database (2008), see Marcel Timmer, Mary O’Mahony and Bart van Ark, The EU

KLEMS Growth and Productivity Accounts: An Overview, Univer sity of Groningen and University

of Birmingham; downloadable at www.euklems.net.

Eurostat, national account data, see http ://epp.eurostat.ec.europa.eu/portal/page /portal/national

accounts/data/main tables, Annual National Accounts, National Accounts detailed breakdowns,

National Accounts aggregates and employment by branch (NACE).

Haaland, J I; Kind, H J; Midelfart-Knarvik, K H; Torstensson, J (1999), “What determines the

Economic Geography of Europe?”, CEPR discussion paper No. 2072.

Hallet, M (2000), “Regional Specialization and Concentration in the EU”, EC Economic Papers.

Jennequin, H (2008), ”The Evolution of the Geographical Concentration of Tertiary Sector Activities

in Europe”, The Service Industries Journal, Vol. 28, No. 3, pages 291-306.

Kim, S (1995), ”Expansion of Markets and the Geographic Distribution of Economic Activities: The

Trends in U.S. Regional Manufacturing Structure, 1860-1987”, The Quarterly Journal of Economics,

Vol. 110, No. 4, pages 881-908.

Kolko, J (2010), “Urbanization, Agglomeration, and Coagglomeration of Service Industries”, in

Agglomeration Economics , ed.: Glaeser, E L, The University of Chicago Press, pages 151-180.

Krugman, P; Venables, A (1996), ”Integration, Specialization and Adjustment”, European Economic

Review, Vol. 40, pages 959-967.

Krugman, P; Venables, A (1995), ”Globalization and the Inequality of Nations”, The Quarterly

Journal of Economics, Vol. 110, No. 4, pages 857-880.

Krugman, P (1991 b), ”Increasing Returns and Economic Geography”, Journal of Political Economy,

Vol. 99, No. 3, pages 483-499.

Krugman, P (1991 a), Geography and Trade, MIT Press, Cambridge, Massachusetts.

Krugman, P (1980), ”Scale Economies, Product Differentiation, and the Pattern of Trade”, The

American Economic Review, Vol. 70, No. 5, pages 950-959.

Krugman, P (1979), ”Increasing Returns, Monopolistic Competition, and International Trade”, Journal

of International Economics, Vol. 9, pages 469-479.

Marshall, A (1938), Principles of Economics, 8. edition, London, Macmillan.

Midelfart-Knarvik, K H; Overman, H G; Redding, S J; Venables, A J (2000), ”The Location of

European Industry”, European Economy-Economic Papers, No. 142.

Oros, C.; Turcu, C. R. (2008), “How does sector concentration evolve at country and regional levels?

The European case”, The Amfiteatru Economic Journal, 10, 24, pages 273-282.

Paluzie, E; Pons, J; Tirado, D (2001), ”Regional Integration and Specialization Patterns in Spain”,

Regional Studies, Vol. 35, No. 4, pages 285-296.

Rosenthal, S; Strange, W (2001), “ The determinants of agglomeration”, Journal of Urban Economics,

Vol. 50, No. 2, pages 191-229.

Torstensson, J (1996), “Technical Differences and Inter-Industry Trade in the Nordic Countries”,

Scandinavian Journal of Economics, Vol. 98, No.1, pp. 93-110.

Bisher erschienene Diskussionspapiere

Nr. 154: Krenz, Astrid: Services Sector’s Concentration: the European Union, Greece, and the New Economic Geography, Mai 2013

Nr. 153: Ahmed, Junaid; Martinez-Zarzoso, Inmaculada: Blessing or Curse: The Stabilizing Role of Remittances, Foreign Aid and FDI to Pakistan, Mai 2013

Nr: 152: Strulik, Holger; Werner, Katharina: 50 is the New 30 – Long-run Trends of Schooling and Retirement Explained by Human Aging, März 2013

Nr. 151: Dalgaard, Carl-Johan; Strulik,Holger: The History Augmented Solow Model, März 2013

Nr. 150: Strulik, Holger; Trimborn, Timo: The Dark Side of Fiscal Stimulus, Januar 2013

Nr. 149: Prettner, Klaus: Public education, technological change and economic prosperity, Januar 2013

Nr. 148: Lankau, Matthias; Bicskei, Marianna; Bizer, Kilian: Cooperation Preferences in the Provision of Public Goods: An Experimental Study on the Effects of Social Identity, Dezember 2012

Nr. 147: Krenz, Astrid: Modeling Services Sectors' Agglomeration within a New Economic Geography Model, Dezember 2012

Nr. 146: Krenz, Astrid: A Panel Co-integration Analysis of Industrial and Services Sectors' Agglomeration in the European Union, Dezember 2012

Nr. 145: Strulik, Holger: Knowledge and Growth in the Very Long Run, November 2012

Nr. 144: Baskaran, Thushyanthan: Ideology and fiscal policy: quasi-experimental evidence from the German States, Oktober 2012

Nr. 143: Ehlers, Tim; Schwager, Robert: Honest Grading, Grade Inflation and Reputation, Oktober 2012

Nr. 142: Gehringer, Agnieszka: Another look at the determinants of current account imbalances in the European Union: An empirical assessment, Oktober 2012

Nr. 141: Strulik, Holger; Werner, Katharina: Life Expectancy, Labor Supply, and Long-Run Growth: Reconciling Theory and Evidence, September 2012

Nr. 140: Strulik, Holger; Prettner, Klaus; Prskawetz, Alexia: The Past and Future of Knowledge-based Growth, September 2012

Nr. 139: Prettner, Klaus; Trimborn, Timo: Demographic change and R&D-based economic growth: reconciling theory and evidence, September 2012

Nr. 138: König, Jörg; Ohr, Renate: Homogeneous groups within a heterogeneous community -Evidence from an index measuring European economic integration, August 2012

Nr. 137: Schwager, Robert: Student Loans in a Tiebout Model of Higher Education, Juli 2012

Nr. 136: Martínez-Zarzoso, Inmaculada: Exporting and Productivity: Evidence for Egypt and Morocco, April 2012

Nr. 135: König, Jörg; Ohr, Renate: Messung ökonomischer Integration in der Europäischen Union –Entwicklung eines EU-Integrationsindexes -, April 2012

Nr. 134: Gehringer, Agnieszka: Financial liberalization, growth, productivity and capital accumulation: The case of European integration, März 2012

Nr. 133: Berner, Eike; Birg, Laura: Retailers and Consumers. The pass-through of import price changes, März 2012

Nr. 132: Gehringer, Angnieszka: Current accounts in Europe: implications of the external imbalances for the future of the common monetary policy, März 2012

Nr. 131: Ohr, Renate; Özalbayrak, Mehmet: The Euro – A „MUST“ for Small European States?, Januar 2012

Nr. 130: Zeddies, Götz: Der Euro als Triebfeder des deutschen Exports?, November 2011

Nr. 129: Geishecker, Ingo; Siedler, Thomas: Job Loss Fears and (Extreme) Party Identification: First Evidence from Panel Data, Oktober 2011

Nr. 128: König, Jörg; Ohr, Renate: Small but Beautiful? Economic Impacts of the Size of Nations in the European Union, August 2011

Nr. 127: Schüder, Stefan: Monetary Policy Trade-Offs in a Portfolio Model with Endogenous Asset Supply, Juni 2011

Nr. 126: Hiller, Sanne: The Export Promoting Effect of Emigration: Evidence from Denmark, Juni 2011

Nr. 125: Martínez-Zarzoso, Inmaculada; Voicu, Anca M.; Vidovic, Martina: CEECs Integration into Regional and Global Production Networks, Mai 2011

Nr. 124: Roth, Felix; Gros, Daniel; Nowak-Lehmann D., Felicitas: Has the Financial Crisis eroded Citizens’ Trust in the European Central Bank? Panel Data Evidence for the Euro Area, 1999-2011, Mai 2011, Revised Version März 2012

Nr. 123 Dreher, Axel; Vreeland, James Raymond : Buying Votes and International Organizations, Mai 2011

Nr. 122: Schürenberg-Frosch, Hannah: One Model fits all? Determinants of Transport Costs across Sectors and Country Groups, April 2011

Nr. 121: Verheyen, Florian: Bilateral Exports from Euro Zone Countries to the US - Does Exchange Rate Variability Play a Role?, April 2011

Nr. 120: Ehlers, Tim: University Graduation Dependent on Family’s Wealth, Ability and Social Status, April 2011

Nr. 119: Cho, Seo-Young; Dreher, Axel; Neumayer, Eric: The Spread of Anti-trafficking Policies – Evidence from a New Index, März 2011

Nr. 118: Cho, Seo-Young; Vadlamannati, Krishna Chaitanya: Compliance for Big Brothers: An Empirical Analysis on the Impact of the Anti-trafficking Protocol, Februar 2011

Nr. 117: Nunnenkamp, Peter; Öhler, Hannes: Donations to US based NGOs in International Development Cooperation: How (Un-)Informed Are Private Donors?, Februar 2011

Nr. 116: Geishecker, Ingo; Riedl, Maximilian: Ordered Response Models and Non-Random Personality Traits: Monte Carlo Simulations and a Practical Guide, Revised Version Februar 2012

Nr. 115: Dreher, Axel; Gassebner, Martin; Siemers, Lars-H. R.: Globalization, Economic Freedom and Human Rights, Oktober 2010

Nr. 114: Dreher, Axel; Mikosch, Heiner; Voigt, Stefan: Membership has its Privileges – The Effect of Membership in International Organizations on FDI, Oktober 2010

Nr. 113: Fuchs, Andreas; Klann, Nils-Hendrik: Paying a Visit: The Dalai Lama Effect on International Trade, Oktober 2010

Nr. 112: Freitag, Stephan: Choosing an Anchor Currency for the Pacific, Oktober 2010

Nr. 111: Nunnenkamp, Peter; Öhler, Hannes: Throwing Foreign Aid at HIV/AIDS in Developing Countries: Missing the Target?, August 2010

Nr. 110: Ohr, Renate; Zeddies, Götz: „Geschäftsmodell Deutschland“ und außenwirtschaftliche Ungleichgewichte in der EU, Juli 2010

Nr. 109: Nunnenkamp, Peter; Öhler, Hannes: Funding, Competition and the Efficiency of NGOs: An Empirical Analysis of Non-charitable Expenditure of US NGOs Engaged in Foreign Aid, Juli 2010

Nr. 108: Krenz, Astrid: La Distinction reloaded: Returns to Education, Family Background, Cultural and Social Capital in Germany, Juli 2010

Nr. 107: Krenz, Astrid: Services sectors' agglomeration and its interdependence with industrial agglomeration in the European Union, Juli 2010

Nr. 106: Krenz, Astrid; Rübel, Gerhard: Industrial Localization and Countries' Specialization in the European Union: An Empirical Investigation, Juli 2010

Nr. 105: Schinke, Jan Christian: Follow the Sun! How investments in solar power plants in Sicily can generate high returns of investments and help to prevent global warming, Juni 2010

Nr. 104: Dreher, Axel; Sturm, Jan-Egbert; Vreeland, James Raymon: Does membership on the Security Council influence IMF conditionality?, Juni 2010

Nr. 103: Öhler, Hannes; Nunnenkamp, Peter; Dreher, Axel: Does Conditionality Work? A Test for an Innovative US Aid Scheme, Juni 2010

Nr. 102: Gehringer, Agnieszka: Pecuniary Knowledge Externalities in a New Taxonomy: Knowledge Interactions in a Vertically Integrated System, Juni 2010

Nr. 101: Gehringer, Agnieszka: Pecuniary Knowledge Externalities across European Countries – are there leading Sectors?, Juni 2010

Nr. 100: Gehringer, Agnieszka: Pecuniary Knowledge Externalities and Innovation: Intersectoral Linkages and their Effects beyond Technological Spillovers, Juni 2010

Nr. 99: Dreher, Axel; Nunnenkamp, Peter; Öhler, Hannes: Why it pays for aid recipients to take note of the Millennium Challenge Corporation: Other donors do!, April 2010

Nr. 98: Baumgarten, Daniel; Geishecker, Ingo; Görg, Holger: Offshoring, tasks, and the skill-wage pattern, März 2010

Nr. 97: Dreher, Axel; Klasen, Stephan; Raymond, James; Werker, Eric: The costs of favoritism: Is politically-driven aid less effective?, März 2010

Nr. 96: Dreher, Axel; Nunnenkamp, Peter; Thiele, Rainer: Are ‘New’ Donors Different? Comparing the Allocation of Bilateral Aid between Non-DAC and DAC Donor Countries, März 2010

Nr. 95: Lurweg, Maren; Westermeier, Andreas: Jobs Gained and Lost through Trade – The Case of Germany, März 2010

Nr. 94: Bernauer, Thomas; Kalbhenn, Anna; Koubi, Vally; Ruoff, Gabi: On Commitment Levels and Compliance Mechanisms – Determinants of Participation in Global Environmental Agreements, Januar 2010

Nr. 93: Cho, Seo-Young: International Human Rights Treaty to Change Social Patterns – The Convention on the Elimination of All Forms of Discrimination against Women, Januar 2010

Nr. 92: Dreher, Axel; Nunnenkamp, Peter; Thiel, Susann; Thiele, Rainer: Aid Allocation by German NGOs: Does the Degree of Public Refinancing Matter?, Januar 2010

Nr. 91: Bjørnskov, Christian; Dreher, Axel; Fischer, Justina A. V.; Schnellenbach, Jan: On the relation between income inequality and happiness: Do fairness perceptions matter?, Dezember 2009

Nr. 90: Geishecker, Ingo: Perceived Job Insecurity and Well-Being Revisited: Towards Conceptual Clarity, Dezember 2009

Nr. 89: Kühl, Michael: Excess Comovements between the Euro/US dollar and British pound/US dollar exchange rates, November 2009

Nr. 88: Mourmouras, Alex, Russel, Steven H.: Financial Crises, Capital Liquidation and the Demand for International Reserves, November 2009

Nr. 87: Goerke, Laszlo, Pannenberg, Markus: An Analysis of Dismissal Legislation: Determinants of Severance Pay in West Germany, November 2009

Nr. 86: Marchesi, Silvia, Sabani, Laura, Dreher, Axel: Read my lips: the role of information transmission in multilateral reform design, Juni 2009

Nr. 85: Heinig, Hans Michael: Sind Referenden eine Antwort auf das Demokratiedilemma der EU?, Juni 2009

Nr. 84: El-Shagi, Makram: The Impact of Fixed Exchange Rates on Fiscal Discipline, Juni 2009

Nr. 83: Schneider, Friedrich: Is a Federal European Constitution for an Enlarged European Union Necessary? Some Preliminary Suggestions using Public Choice Analysis, Mai 2009

Nr. 82: Vaubel, Roland: Nie sollst Du mich befragen? Weshalb Referenden in bestimmten Politikbereichen – auch in der Europapolitik – möglich sein sollten, Mai 2009

Nr. 81: Williamson, Jeffrey G.: History without Evidence: Latin American Inequality since 1491, Mai 2009

Nr. 80: Erdogan, Burcu: How does the European Integration affect the European Stock Markets?, April 2009

Nr. 79: Oelgemöller, Jens; Westermeier, Andreas: RCAs within Western Europe, März 2009

Nr. 78: Blonski, Matthias; Lilienfeld-Toal, Ulf von: Excess Returns and the Distinguished Player Paradox, Oktober 2008

Nr. 77: Lechner, Susanne; Ohr, Renate: The Right of Withdrawal in the Treaty of Lisbon: A game theoretic reflection on different decision processes in the EU, Oktober 2008

Nr. 76: Kühl, Michael: Strong comovements of exchange rates: Theoretical and empirical cases when currencies become the same asset, Juli 2008

Nr. 75: Höhenberger, Nicole; Schmiedeberg, Claudia: Structural Convergence of European Countries, Juli 2008

Nr. 74: Nowak-Lehmann D., Felicitas; Vollmer, Sebastian; Martinez-Zarzoso, Inmaculada: Does Comparative Advantage Make Countries Competitive? A Comparison of China and Mexico, Juli 2008

Nr. 73: Fendel, Ralf; Lis, Eliza M.; Rülke, Jan-Christoph: Does the Financial Market Believe in the Phillips Curve? – Evidence from the G7 countries, Mai 2008

Nr. 72: Hafner, Kurt A.: Agglomeration Economies and Clustering – Evidence from German Firms, Mai 2008

Nr. 71: Pegels, Anna: Die Rolle des Humankapitals bei der Technologieübertragung in Entwicklungsländer, April 2008

Nr. 70: Grimm, Michael; Klasen, Stephan: Geography vs. Institutions at the Village Level, Februar 2008

Nr. 69: Van der Berg, Servaas: How effective are poor schools? Poverty and educational outcomes in South Africa, Januar 2008

Nr. 68: Kühl, Michael: Cointegration in the Foreign Exchange Market and Market Efficiency since the Introduction of the Euro: Evidence based on bivariate Cointegration Analyses, Oktober 2007

Nr. 67: Hess, Sebastian; Cramon-Taubadel, Stephan von: Assessing General and Partial Equilibrium Simulations of Doha Round Outcomes using Meta-Analysis, August 2007

Nr. 66: Eckel, Carsten: International Trade and Retailing: Diversity versus Accessibility and the Creation of “Retail Deserts”, August 2007

Nr. 65: Stoschek, Barbara: The Political Economy of Enviromental Regulations and Industry Compensation, Juni 2007

Nr. 64: Martinez-Zarzoso, Inmaculada; Nowak-Lehmann D., Felicitas; Vollmer, Sebastian: The Log of Gravity Revisited, Juni 2007

Nr. 63: Gundel, Sebastian: Declining Export Prices due to Increased Competition from NIC – Evidence from Germany and the CEEC, April 2007

Nr. 62: Wilckens, Sebastian: Should WTO Dispute Settlement Be Subsidized?, April 2007

Nr. 61: Schöller, Deborah: Service Offshoring: A Challenge for Employment? Evidence from Germany, April 2007

Nr. 60: Janeba, Eckhard: Exports, Unemployment and the Welfare State, März 2007

Nr. 59: Lambsdoff, Johann Graf; Nell, Mathias: Fighting Corruption with Asymmetric Penalties and Leniency, Februar 2007

Nr. 58: Köller, Mareike: Unterschiedliche Direktinvestitionen in Irland – Eine theoriegestützte Analyse, August 2006

Nr. 57: Entorf, Horst; Lauk, Martina: Peer Effects, Social Multipliers and Migrants at School: An International Comparison, März 2007 (revidierte Fassung von Juli 2006)

Nr. 56: Görlich, Dennis; Trebesch, Christoph: Mass Migration and Seasonality Evidence on Moldova’s Labour Exodus, Mai 2006

Nr. 55: Brandmeier, Michael: Reasons for Real Appreciation in Central Europe, Mai 2006

Nr. 54: Martínez-Zarzoso, Inmaculada; Nowak-Lehmann D., Felicitas: Is Distance a Good Proxy for Transport Costs? The Case of Competing Transport Modes, Mai 2006

Nr. 53: Ahrens, Joachim; Ohr, Renate; Zeddies, Götz: Enhanced Cooperation in an Enlarged EU, April 2006

Nr. 52: Stöwhase, Sven: Discrete Investment and Tax Competition when Firms shift Profits, April 2006

Nr. 51: Pelzer, Gesa: Darstellung der Beschäftigungseffekte von Exporten anhand einer Input-Output-Analyse, April 2006

Nr. 50: Elschner, Christina; Schwager, Robert: A Simulation Method to Measure the Tax Burden on Highly Skilled Manpower, März 2006

Nr. 49: Gaertner, Wulf; Xu, Yongsheng: A New Measure of the Standard of Living Based on Functionings, Oktober 2005

Nr. 48: Rincke, Johannes; Schwager, Robert: Skills, Social Mobility, and the Support for the Welfare State, September 2005

Nr. 47: Bose, Niloy; Neumann, Rebecca: Explaining the Trend and the Diversity in the Evolution of the Stock Market, Juli 2005

Nr. 46: Kleinert, Jörn; Toubal, Farid: Gravity for FDI, Juni 2005

Nr. 45: Eckel, Carsten: International Trade, Flexible Manufacturing and Outsourcing, Mai 2005

Nr. 44: Hafner, Kurt A.: International Patent Pattern and Technology Diffusion, Mai 2005

Nr. 43: Nowak-Lehmann D., Felicitas; Herzer, Dierk; Martínez-Zarzoso, Inmaculada; Vollmer, Sebastian: Turkey and the Ankara Treaty of 1963: What can Trade Integration Do for Turkish Exports, Mai 2005

Nr. 42: Südekum, Jens: Does the Home Market Effect Arise in a Three-Country Model?, April 2005

Nr. 41: Carlberg, Michael: International Monetary Policy Coordination, April 2005

Nr. 40: Herzog, Bodo: Why do bigger countries have more problems with the Stability and Growth Pact?, April 2005

Nr. 39: Marouani, Mohamed A.: The Impact of the Mulitfiber Agreement Phaseout on Unemployment in Tunisia: a Prospective Dynamic Analysis, Januar 2005

Nr. 38: Bauer, Philipp; Riphahn, Regina T.: Heterogeneity in the Intergenerational Transmission of Educational Attainment: Evidence from Switzerland on Natives and Second Generation Immigrants, Januar 2005

Nr. 37: Büttner, Thiess: The Incentive Effect of Fiscal Equalization Transfers on Tax Policy, Januar 2005

Nr. 36: Feuerstein, Switgard; Grimm, Oliver: On the Credibility of Currency Boards, Oktober 2004

Nr. 35: Michaelis, Jochen; Minich, Heike: Inflationsdifferenzen im Euroraum – eine Bestandsaufnahme, Oktober 2004

Nr. 34: Neary, J. Peter: Cross-Border Mergers as Instruments of Comparative Advantage, Juli 2004

Nr. 33: Bjorvatn, Kjetil; Cappelen, Alexander W.: Globalisation, inequality and redistribution, Juli 2004

Nr. 32: Stremmel, Dennis: Geistige Eigentumsrechte im Welthandel: Stellt das TRIPs-Abkommen ein Protektionsinstrument der Industrieländer dar?, Juli 2004

Nr. 31: Hafner, Kurt: Industrial Agglomeration and Economic Development, Juni 2004

Nr. 30: Martinez-Zarzoso, Inmaculada; Nowak-Lehmann D., Felicitas: MERCOSUR-European Union Trade: How Important is EU Trade Liberalisation for MERCOSUR’s Exports?, Juni 2004

Nr. 29: Birk, Angela; Michaelis, Jochen: Employment- and Growth Effects of Tax Reforms, Juni 2004

Nr. 28: Broll, Udo; Hansen, Sabine: Labour Demand and Exchange Rate Volatility, Juni 2004

Nr. 27: Bofinger, Peter; Mayer, Eric: Monetary and Fiscal Policy Interaction in the Euro Area with different assumptions on the Phillips curve, Juni 2004

Nr. 26: Torlak, Elvisa: Foreign Direct Investment, Technology Transfer and Productivity Growth in Transition Countries, Juni 2004

Nr. 25: Lorz, Oliver; Willmann, Gerald: On the Endogenous Allocation of Decision Powers in Federal Structures, Juni 2004

Nr. 24: Felbermayr, Gabriel J.: Specialization on a Technologically Stagnant Sector Need Not Be Bad for Growth, Juni 2004

Nr. 23: Carlberg, Michael: Monetary and Fiscal Policy Interactions in the Euro Area, Juni 2004

Nr. 22: Stähler, Frank: Market Entry and Foreign Direct Investment, Januar 2004

Nr. 21: Bester, Helmut; Konrad, Kai A.: Easy Targets and the Timing of Conflict, Dezember 2003

Nr. 20: Eckel, Carsten: Does globalization lead to specialization, November 2003

Nr. 19: Ohr, Renate; Schmidt, André: Der Stabilitäts- und Wachstumspakt im Zielkonflikt zwischen fiskalischer Flexibilität und Glaubwürdigkeit: Ein Reform-ansatz unter Berücksichtigung konstitutionen- und institutionenökonomischer Aspekte, August 2003

Nr. 18: Ruehmann, Peter: Der deutsche Arbeitsmarkt: Fehlentwicklungen, Ursachen und Reformansätze, August 2003

Nr. 17: Suedekum, Jens: Subsidizing Education in the Economic Periphery: Another Pitfall of Regional Policies?, Januar 2003

Nr. 16: Graf Lambsdorff, Johann; Schinke, Michael: Non-Benevolent Central Banks, Dezember 2002

Nr. 15: Ziltener, Patrick: Wirtschaftliche Effekte des EU-Binnenmarktprogramms, November 2002

Nr. 14: Haufler, Andreas; Wooton, Ian: Regional Tax Coordination and Foreign Direct Investment, November 2001

Nr. 13: Schmidt, André: Non-Competition Factors in the European Competition Policy: The Necessity of Institutional Reforms, August 2001

Nr. 12: Lewis, Mervyn K.: Risk Management in Public Private Partnerships, Juni 2001

Nr. 11: Haaland, Jan I.; Wooton, Ian: Multinational Firms: Easy Come, Easy Go?, Mai 2001

Nr. 10: Wilkens, Ingrid: Flexibilisierung der Arbeit in den Niederlanden: Die Entwicklung atypischer Beschäftigung unter Berücksichtigung der Frauenerwerbstätigkeit, Januar 2001

Nr. 9: Graf Lambsdorff, Johann: How Corruption in Government Affects Public Welfare – A Review of Theories, Januar 2001

Nr. 8: Angermüller, Niels-Olaf: Währungskrisenmodelle aus neuerer Sicht, Oktober 2000

Nr. 7: Nowak-Lehmann, Felicitas: Was there Endogenous Growth in Chile (1960-1998)? A Test of the AK model, Oktober 2000

Nr. 6: Lunn, John; Steen, Todd P.: The Heterogeneity of Self-Employment: The Example of Asians in the United States, Juli 2000

Nr. 5: Güßefeldt, Jörg; Streit, Clemens: Disparitäten regionalwirtschaftlicher Entwicklung in der EU, Mai 2000

Nr. 4: Haufler, Andreas: Corporate Taxation, Profit Shifting, and the Efficiency of Public Input Provision, 1999

Nr. 3: Rühmann, Peter: European Monetary Union and National Labour Markets, September 1999

Nr. 2: Jarchow, Hans-Joachim: Eine offene Volkswirtschaft unter Berücksichtigung des Aktienmarktes, 1999

Nr. 1: Padoa-Schioppa, Tommaso: Reflections on the Globalization and the Europeanization of the Economy, Juni 1999

Alle bisher erschienenen Diskussionspapiere zum Download finden Sie im Internet unter: http://www.uni-goettingen.de/de/60920.html.